Help with DATA 330 Data Quality Report

Executive Summary: Provide a brief overview of the data quality issues, their potential impact on the organization, and recommended actions to address them.

Data Background: Describe the data source, the context in which it was collected, and any known constraints or limitations.

Problem Statement: Clearly state the data quality issues identified, including details on the issues (e.g., missing values, outliers, inconsistencies) and their potential implications for data analysis and decision-making.

Methodology: Explain the data preparation and exploration methods used to identify the data quality issues, such as data profiling, statistical analysis, and data visualization techniques.

Findings: Present the results of the data exploration and analysis using tables, charts, and graphs to illustrate the data quality issues. This could include frequency distributions, outlier detection analyses, or summary statistics.

Recommendations: Specific recommendations for addressing the data quality issues, such as data cleaning techniques, outlier handling strategies, or additional data collection efforts. Include the expected benefits of these actions, such as improved data accuracy and reliability or better decision-making capabilities.